Many artificial intelligence startup ideas are still little more than superficial “wrappers” built on top of existing models. As the AI model makers add more features, investors are wary of startups that could become so easily unnecessary.
This was evident when reviewing over 4,000 applications for the joint AI accelerator for Indian startups run by Google and venture firm Accel. “Wrapper” ideas dominated the applications, but none were selected for the latest cohort of five startups, according to Accel partner Prayank Swaroop.
Announced in November, the AI-focused Atoms program by Google and Accel aims to back early-stage startups building AI products linked to India. Startups selected for the latest cohort will receive up to $2 million in funding from Accel and Google’s AI Futures Fund, along with up to $350,000 in cloud and AI compute credits from Google.
Roughly 70% of the rejected applications were “wrappers.” These were startups that layered AI features such as chatbots on top of existing software but were not reimagining new workflows using AI. Many of the remaining denied applications fell into crowded categories like marketing automation and AI recruitment tools, areas where investors saw little novelty. Startups in those sectors often struggle to differentiate themselves.
This year’s program received nearly four times the applications of previous Accel Atoms cohorts, with many first-time founders. India’s growing AI ecosystem remains largely focused on enterprise applications, and the applications reflected that. About 62% of submissions focused on productivity tools and another 13% on software development and coding, meaning around three-quarters were enterprise software ideas rather than consumer products.
Jonathan Silber, co-founder and director of Google’s AI Futures Fund, said the five selected startups aligned closely with areas where Google expects AI to see deeper real-world adoption. The program does not require startups to use Google’s models exclusively, noting that many companies combine multiple models depending on the workflow. The goal is to gather feedback from startups on how Google’s models perform in real-world applications.
Insights from those startups can then be fed back to Google DeepMind teams to help improve future models, creating a “flywheel” between startup experimentation and AI development. If a company is using an alternative model, that means Google has work to do to build the best model in the market.
This year’s selected startups are: K-Dense, which is building an AI “co-scientist” to accelerate research in fields such as life sciences and chemistry; Dodge.ai, which develops autonomous agents for enterprise ERP systems; Persistence Labs, which focuses on voice AI for call centre operations; Zingroll, which is building a platform for AI-generated films and shows; and Level Plane, which applies AI to industrial automation in automotive and aerospace manufacturing.